A Comparison of Estimators for Multivariate ARCH Models

Polasek, W.; Liu, S.

doi:10.1007/978-3-642-55991-4_40

W. Polasek⁶ &
S. Liu⁶

Part of the book series: Studies in Classification, Data Analysis, and Knowledge Organization ((STUDIES CLASS))

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Abstract

Multivariate or vector autoregressive conditional heteroskedastic (VARCH) models have become increasingly important for applications in several fields of economics and finance. This paper compares the method of scoring for maximum likelihood estimation (MLE) of (1999) with currently used alternative estimators. We estimate a bivariate VAR-VARCH model with a simulated data set and compare the method with classical and Bayesian methods which are available in econometric program packages.

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Authors and Affiliations

Institute of Statistics and Econometrics, University of Basel, Bernoullistrasse 28, CH-4051, Basel, Switzerland
W. Polasek & S. Liu

Authors

W. Polasek
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S. Liu
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Editors and Affiliations

Institute for Decision Theory and Operations Research, University of Karlsruhe, Kaiserstraße 12, 76128, Karlsruhe, Germany
Wolfgang Gaul
Department of Mathematics and Informatics, University of Passau, Innstraße 33, 94030, Passau, Germany
Gunter Ritter

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Polasek, W., Liu, S. (2002). A Comparison of Estimators for Multivariate ARCH Models. In: Gaul, W., Ritter, G. (eds) Classification, Automation, and New Media. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-55991-4_40

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DOI: https://doi.org/10.1007/978-3-642-55991-4_40
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-43233-3
Online ISBN: 978-3-642-55991-4
eBook Packages: Springer Book Archive

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